基于遗传算法DDBN参数学习的UUV威胁评估  

Threat assessment of UUV based on genetic algorithm DDBN parameter learning

作　　者：么洪飞[1,2] 王宏健 王莹[1,3] 李庆[1] YAO Hongfei;WANG Hongjian;WANG Ying;LI Qing(College of Automation,Harbin Engineering University,Harbin 150001;College of Mechanical Engineering,Qiqihar University,Qiqihar 161006,China;Economic Research Institute,State Grid Heilongjiang Electric Power Company Limited,Harbin 150036,China)

机构地区：[1]哈尔滨工程大学自动化学院,黑龙江哈尔滨150001 [2]齐齐哈尔大学机电工程学院,黑龙江齐齐哈尔161006 [3]国网黑龙江省电力有限公司经济技术研究院,黑龙江哈尔滨150036

出　　处：《哈尔滨工程大学学报》2018年第12期1972-1978,共7页Journal of Harbin Engineering University

基　　金：国家自然科学基金重点项目(61633008);黑龙江省自然科学基金面上项目(F2015035);哈尔滨市科技创新人才(优秀学科带头人)研究专项基金项目(2012RFXXG083)

摘　　要：针对复杂海洋环境下存在不确定事件对无人水下航行器自主作业和安全性所带来的威胁。本文设计了基于动态贝叶斯网络的威胁评估模型和决策推理模型,采用遗传算法实现了离散动态贝叶斯网络参数学习,最终得到最优的模型参数,进而增强了推理模型对海洋环境的快速反应能力。仿真实验结果证明:提出的算法可以得到真实的离散动态贝叶斯网络参数,能够有效地解决复杂海洋环境下UUV威胁评估问题,为UUV的自主任务决策提供有效的参数保障。Considering the uncertain events in complex marine environments,threat assessment is very important for the safety of unmanned underwater vehicles(UUVs)during autonomous operations.In this study,a threat assessment model and a decision reasoning model are designed based on dynamic Bayesian network.The genetic algorithm is used to realize the discrete dynamic Bayesian network(DDBN)parameter learning,and finally,the optimal model parameters are obtained;this enhances the rapid response of the reasoning model to the marine environment.The simulation experiment results show that the real DDBN parameters can be obtained using the proposed algorithm.Thus,the UUV threat assessment problem in complex marine environment can be effectively solved,and the parameters for optimal UUV autonomous decision making can be provided.

关 键 词：参数学习 威胁评估 离散动态贝叶斯网络 遗传算法 无人水下航行器 决策 

分 类 号：U666[交通运输工程—船舶及航道工程]